Bibliographic record
Abstract
Following decades of government mismanagement and corruption at Beirut’s port, on August 4, 2020, one of the largest non-nuclear explosions in history pulverized the port and damaged over half the city. The explosion resulted from the detonation of tonnes of ammonium nitrate, a combustible chemical compound commonly used in agriculture as a high nitrate fertilizer, but which can also be used to manufacture explosives. The cargo of ammonium nitrate had entered Beirut’s port on a Moldovan-flagged ship, the Rhosus, in November 2013, and had been offloaded into hangar 12 in Beirut’s port on October 23 and 24, 2014. The Beirut port explosion killed 218 people, including nationals of Lebanon, Syria, Egypt, Ethiopia, Bangladesh, Philippines, Pakistan, Palestine, the Netherlands, Canada, Germany, France, Australia, and the United States. It wounded 7,000 people, of whom at least 150 acquired a physical disability; caused untold psychological harm; and damaged 77,000 apartments, displacing over 300,000 people. At least three children between the ages of 2 and 15 lost their lives. Thirty-one children required hospitalization, 1,000 children were injured, and 80,000 children were left without a home. The explosion affected 163 public and private schools and rendered half of Beirut’s healthcare centers nonfunctional, and it impacted 56 percent of the private businesses in Beirut. There was extensive damage to infrastructure, including transport, energy, water supply and sanitation, and municipal services totaling US$390-475 million in losses. According to the World Bank, the explosion caused an estimated $3.8-4.6 billion in material damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.099 | 0.054 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".